Researchers introduced ALoDLM (Adaptively Looped Diffusion Language Models), a method that attributes the quality gap between diffusion language models and comparably sized autoregressive models to a computation-difficulty mismatch and addresses it by adaptively looping computation. Diffusion language models generate fast by predicting multiple tokens in parallel, but their practical adoption remains limited by that persistent quality gap.
Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a persistent quality gap relative to comparably sized autoregressive (AR) models. We attribute this gap to a computation-difficulty mismatch: within a pa
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